Data Engineering Podcast

MAY 26, 2024

Announcements Hello and welcome to the Data Engineering Podcast, the show about modern data management Data lakes are notoriously complex. Can you start by sharing some of your experiences with data migration projects? Closing Announcements Thank you for listening! Don't forget to check out our other shows.

Systems 130

Systems Data Lake High Quality Data Google Cloud 130

Precisely

NOVEMBER 18, 2024

However, they require a strong data foundation to be effective. With the rise of cloud-based data management, many organizations face the challenge of accessing both on-premises and cloud-based data. Without a unified, clean data structure, leveraging these diverse data sources is often problematic.

Data Analytics 57

Data Analytics Data Governance Government Data Integration 57

DataKitchen

APRIL 13, 2021

Databand — Data pipeline performance monitoring and observability for data engineering teams. . Soda Data Monitoring — Soda tells you which data is worth fixing. Soda doesn’t just monitor datasets and send meaningful alerts to the relevant teams. Observe, optimize, and scale enterprise data pipelines. .

Consulting 121

Consulting Machine Learning Data Science Data Pipeline 121

Databand.ai

AUGUST 30, 2023

These tools play a vital role in data preparation, which involves cleaning, transforming, and enriching raw data before it can be used for analysis or machine learning models. There are several types of data testing tools.

Data Cleanse 52

Data Cleanse Data Validation Data Pipeline Datasets 52

Rockset

MARCH 1, 2023

The concurrent queries will not see the effect of the data loads until the data load is complete, creating 10s of minutes of data lags. OLTP databases aren’t built to ingest massive volumes of data streams and perform stream processing on incoming datasets. So they are not suitable for real-time analytics.

Data Ingestion 144

Data Ingestion Database Architecture SQL 144

Monte Carlo

JANUARY 10, 2024

Data Profiling 2. Data Cleansing 3. Data Validation 4. Data Auditing 5. Data Governance 6. Use of Data Quality Tools Refresh your intrinsic data quality with data observability 1. Data Profiling Data profiling is getting to know your data, warts and quirks and secrets and all.

Data Cleanse 52

Data Cleanse Data Engineering Data Engineer Engineering 52

Monte Carlo

OCTOBER 10, 2024

Table of Contents What Does an AI Data Quality Analyst Do? Essential Skills for an AI Data Quality Analyst There are several important skills an AI Data Quality Analyst needs to know in order to successfully ensure and maintain accurate, reliable AI models. Machine Learning Basics : Understanding how data impacts model training.

Unstructured Data 52

Unstructured Data Google Cloud Machine Learning ETL Tools 52

The Modern Data Company

AUGUST 13, 2024

As organizations seek to leverage data more effectively, the focus has shifted from temporary datasets to well-defined, reusable data assets. Data products transform raw data into actionable insights, integrating metadata and business logic to meet specific needs and drive strategic decision-making.

Raw Data 52

Raw Data Metadata Datasets Utilities 52

Databand.ai

AUGUST 30, 2023

These tools play a vital role in data preparation, which involves cleaning, transforming and enriching raw data before it can be used for analysis or machine learning models. There are several types of data testing tools.

Data Cleanse 52

Data Cleanse Data Pipeline Datasets Data Validation 52

Databand.ai

AUGUST 30, 2023

This includes defining roles and responsibilities related to managing datasets and setting guidelines for metadata management. Data profiling: Regularly analyze dataset content to identify inconsistencies or errors. Additionally, high-quality data reduces costly errors stemming from inaccurate information.

Building 52

Building Data Cleanse Data Governance Datasets 52

The Modern Data Company

JULY 18, 2023

High-quality data, free from errors, inconsistencies, or biases, forms the foundation for accurate analysis and reliable insights. Data products should incorporate mechanisms for data validation, cleansing, and ongoing monitoring to maintain data integrity.

Data Governance 69

Data Governance High Quality Data Government Data 69

Knowledge Hut

JANUARY 25, 2024

What is Data Cleaning? Data cleaning, also known as data cleansing, is the essential process of identifying and rectifying errors, inaccuracies, inconsistencies, and imperfections in a dataset. It involves removing or correcting incorrect, corrupted, improperly formatted, duplicate, or incomplete data.

Data Cleanse 52

Data Cleanse Datasets Data Preparation Data Science 52

Databand.ai

JULY 6, 2023

By routinely conducting data integrity tests, organizations can detect and resolve potential issues before they escalate, ensuring that their data remains reliable and trustworthy. Data integrity monitoring can include periodic data audits, automated data integrity checks, and real-time data validation.

Data Integration 52

Data Integration Process Data Validation Data Governance 52

Knowledge Hut

JULY 26, 2023

Consider exploring relevant Big Data Certification to deepen your knowledge and skills. What is Big Data? Big Data is the term used to describe extraordinarily massive and complicated datasets that are difficult to manage, handle, or analyze using conventional data processing methods.

Big Data 52

Big Data Data Cleanse Retail Healthcare 52

Precisely

AUGUST 29, 2024

To maximize your investments in AI, you need to prioritize data governance, quality, and observability. Solving the Challenge of Untrustworthy AI Results AI has the potential to revolutionize industries by analyzing vast datasets and streamlining complex processes – but only when the tools are trained on high-quality data.

Data Governance 52

Data Governance Government High Quality Data Datasets 52

Ascend.io

APRIL 27, 2023

In this article, we’ll recap the key takeaways from the summit and the groundbreaking advancements in data pipeline automation that we’re working on at Ascend. Link data products within and across data clouds , allowing users to access and analyze data in a unified, consistent manner.

Data Pipeline 52

Data Pipeline Pipeline-centric Data Validation Data Engineering 52

Data Engineering Weekly

OCTOBER 30, 2022

There is no mention of data management in general, but mainly of usage and operational factors. Nothing groundbreaking will happen on data management in 2023, but I expect a little momentum behind data management towards the end.

Data Engineering 52

Data Engineering Data Engineer Engineering Data Ingestion 52

Edureka

AUGUST 2, 2023

Their efforts make ensuring that data is accurate, dependable, and consistent, laying the groundwork for data analysis and decision-making. What does a Data Processing Analysts do ? A data processing analyst’s job description includes a variety of duties that are essential to efficient data management.

Data Process 52

Data Process Process Data Cleanse Data Mining 52

Databand.ai

JULY 3, 2023

The value of that trust is why more and more companies are introducing Chief Data Officers – with the number doubling among the top publicly traded companies between 2019 and 2021, according to PwC. In this article: Why is data reliability important? Note that data validity is sometimes considered a part of data reliability.

Data Validation 52

Data Validation Data Collection Data Machine Learning 52

DataKitchen

JULY 3, 2023

Constant Data And Tool Errors In Production Teams cannot see across all tools, pipelines, jobs, processes, datasets, and people. No Time For Data Validation Testing Teams must learn what, where, and how to check raw, integrated, or ‘data in use’ to ensure the correct outputs.

Data Validation 52

Data Validation Data Data Analytics Data Engineering 52

The Modern Data Company

JULY 18, 2023

High-quality data, free from errors, inconsistencies, or biases, forms the foundation for accurate analysis and reliable insights. Data products should incorporate mechanisms for data validation, cleansing, and ongoing monitoring to maintain data integrity.

Data Governance 52

Data Governance High Quality Data Government Data 52

The Modern Data Company

JULY 18, 2023

High-quality data, free from errors, inconsistencies, or biases, forms the foundation for accurate analysis and reliable insights. Data products should incorporate mechanisms for data validation, cleansing, and ongoing monitoring to maintain data integrity.

Data Governance 52

Data Governance High Quality Data Government Data 52

Databand.ai

JULY 19, 2023

Another way data ingestion enhances data quality is by enabling data transformation. During this phase, data is standardized, normalized, and enriched. Data enrichment involves adding new, relevant information to the existing dataset, which provides more context and improves the depth and value of the data.

Data Ingestion 52

Data Ingestion Process Data Cleanse Data Governance 52

RandomTrees

SEPTEMBER 6, 2020

The key features of the Data Load Accelerator include: Minimal and reusable coding: The model used is configuration-based and all data load requirements will be managed with one code base. Snowflake allows the loading of both structured and semi-structured datasets from cloud storage.

Cloud Storage 52

Cloud Storage Data Ingestion Data Cleanse Data Warehouse 52

Ascend.io

SEPTEMBER 19, 2023

Equipped with built-in connectors, robust error-handling mechanisms, and capabilities to process data in near real-time, automated ETL pipelines not only enhance the speed of data integration but also ensure a higher degree of accuracy and reliability. A more agile, responsive, and error-resistant data management process.

ETL Tools 52

ETL Tools ETL Method Data Engineering Data Engineer 52

Monte Carlo

FEBRUARY 6, 2023

But in reality, a data warehouse migration to cloud solutions like Snowflake and Redshift requires a tremendous amount of preparation to be successful—from schema changes and data validation to a carefully executed QA process. What’s more, issues in the source data could even be amplified by a new, sophisticated system.

Data Warehouse 52

Data Warehouse AWS Data Data Validation 52

AltexSoft

NOVEMBER 23, 2021

As a result, data virtualization enabled the company to conduct advanced analytics and data science, contributing to the growth of the business. Global investment bank: Cost reduction with more scalable and effective data management. How to get started with data virtualization. Know your data sources.

Process 69

Process Data Lake Metadata Data Warehouse 69

Knowledge Hut

SEPTEMBER 25, 2023

Skill Requirements for Azure Data Engineer Job Description Here are some important skill requirements that you may find in a job description for Azure Data Engineers: 1. Create and maintain data storage solutions including Azure SQL Database, Azure Data Lake, and Azure Blob Storage.

Data Engineering 52

Data Engineering Data Engineer Engineering Data Lake 52

ProjectPro

JUNE 18, 2015

said Martha Crow, Senior VP of Global Testing at Lionbridge Big data is all the rage these days as various organizations dig through large datasets to enhance their operations and discover novel solutions to big data problems. We’re looking at the next evolution. a different way of business getting done."-

Big Data 52

Big Data Process Data Cleanse Data Analytics 52

Monte Carlo

MARCH 12, 2024

Google Cloud’s Dataplex is a data fabric tool that enables organizations to discover, manage, monitor, and govern their data across all of their data systems, including their data lakes, data warehouses, data lakehouses, and data marts. Dataplex works with your metadata.

Google Cloud 52

Google Cloud Metadata SQL Data Lake 52

Monte Carlo

OCTOBER 5, 2023

So, in order for your company to uncover the true value of its data, you must take a structured approach to data quality. That’s where data quality standards come into play. Data freshness (aka data timeliness) means your data should be up-to-date and relevant to the timeframe of analysis. name@domain.com).

Government 52

Government Data Governance Data Cloud Storage 52

Precisely

MARCH 14, 2023

New technologies are making it easier for customers to process increasingly large datasets more rapidly. But early adopters realized that the expertise and hardware needed to manage these systems properly were complex and expensive. Design in the cloud, deploy anywhere – with an array of deployment options for complex data processes.

Data Integration 52

Data Integration High Quality Data BI Data 52

Knowledge Hut

OCTOBER 13, 2023

As per Microsoft, “A Power BI report is a multi-perspective view of a dataset, with visuals representing different findings and insights from that dataset. ” Reports and dashboards are the two vital components of the Power BI platform, which are used to analyze and visualize data. Use descriptive names.

BI 52

BI Business Analyst Datasets Raw Data 52

Knowledge Hut

JUNE 30, 2023

It is worth keeping note that the initially described relational model is the most widely used, having different types of relationships in DBMS being supported and preferred data model in today’s modern database systems. Centralization simplifies data management manifold and also improves data consistency while ensuring data integrity.

MySQL 52

MySQL Medical Relational Database Database 52

ProjectPro

JANUARY 31, 2023

Define Big Data and Explain the Seven Vs of Big Data. Big Data is a collection of large and complex semi-structured and unstructured data sets that have the potential to deliver actionable insights using traditional data management tools. MapReduce is a Hadoop framework used for processing large datasets.

Big Data 52

Big Data Hadoop Relational Database AWS 52

Ascend.io

JULY 19, 2023

For one, data mesh tackles the real headaches caused by an overburdened data lake and the annoying game of tag that’s too often played between the people who make data, the ones who use it, and everyone else caught in the middle. This collaboration ensures consistency and compliance across the data mesh implementation.

Data Governance 40

Data Governance Government Metadata Data 40

ProjectPro

FEBRUARY 16, 2023

Learning SQL can help data engineers work more effectively with data analysts and data scientists as they share a common language for querying and analysing data. Data processing tasks containing SQL-based data transformations can be conducted utilizing Hadoop or Spark executors by ETL solutions.

Data Engineering 52

Data Engineering Data Engineer SQL Engineering 52